Cognitive BiasesPublic knowledge library

Practice Lab

AI-assisted decisions

Learn to connect a practical check with the evidence-reviewed lens behind it. A real situation can involve several patterns, so each exercise asks for the best first lens among the listed options, not a diagnosis.

How to use this set

Read the check. Choose a lens. Then inspect the evidence.

Use these evidence-reviewed lenses when a chatbot, model, recommendation system, or automated decision aid is influencing what you believe or do.

Read the full decision guide before or after the set.

Exercise 1 of 6

In ai-assisted decisions, which lens does this check belong to: “Am I using the automated recommendation as a substitute for checking the evidence I could realistically verify?”

  1. Automation Bias
  2. Anchoring Effect
  3. Anthropomorphism
Show the best first lens

Automation Bias

Am I using the automated recommendation as a substitute for checking the evidence I could realistically verify?

Evidence note Automation bias is a documented pattern of inappropriate reliance on automated cues or recommendations. It can produce commission errors when a user follows incorrect advice and omission errors when a user fails to act because automation did not signal a problem. This does not mean automation is generally harmful: decision support can improve overall performance, and the relevant question is whether reliance remains calibrated when the system is wrong, incomplete, or difficult to verify.

Read the evidence review · Open the decision guide

Exercise 2 of 6

In ai-assisted decisions, which lens does this check belong to: “Did the AI’s first number become my starting point before I formed an independent estimate?”

  1. Anthropomorphism
  2. Appearance–Capability Expectation
  3. Anchoring Effect
Show the best first lens

Anchoring Effect

Did the AI’s first number become my starting point before I formed an independent estimate?

Evidence note Anchoring is a well-supported effect in which an initial numerical value can pull a later estimate toward it. A 2026 meta-analysis covering 2,601 effect sizes found a large overall effect, but also substantial variation across studies. The effect should not be treated as a rule that every number changes every judgment: incidental anchors, anchors from a different dimension, clearly random values, incentives, and some debiasing conditions were associated with smaller or null effects.

Read the evidence review · Open the decision guide

Exercise 3 of 6

In ai-assisted decisions, which lens does this check belong to: “Which humanlike cues are making me infer understanding, intention, empathy, or competence that I have not actually tested?”

  1. Confirmation Bias
  2. Anthropomorphism
  3. Appearance–Capability Expectation
Show the best first lens

Anthropomorphism

Which humanlike cues are making me infer understanding, intention, empathy, or competence that I have not actually tested?

Evidence note Anthropomorphism is the attribution of humanlike properties, intentions, emotions, or mental states to nonhuman agents. It is a well-established psychological phenomenon, but it is not automatically a cognitive error: humanlike models can sometimes be useful. The risk appears when humanlike cues are treated as evidence for capabilities, understanding, accuracy, consciousness, or motives that have not actually been demonstrated.

Read the evidence review · Open the decision guide

Exercise 4 of 6

In ai-assisted decisions, which lens does this check belong to: “What capabilities am I inferring from appearance, voice, interface polish, or conversational style rather than observed performance?”

  1. Appearance–Capability Expectation
  2. Confirmation Bias
  3. Illusory Truth Effect
Show the best first lens

Appearance–Capability Expectation

What capabilities am I inferring from appearance, voice, interface polish, or conversational style rather than observed performance?

Evidence note Research supports the broader pattern that a robot's appearance, morphology, framing, and human-likeness shape expectations about its competence, social qualities, and likely behavior. However, 'Form-Function Attribution Bias' is not an established standardized name in the literature. On this site it should be treated as a project label for appearance-driven capability expectations, not as a universally recognized cognitive-bias construct.

Read the evidence review · Open the decision guide

Exercise 5 of 6

In ai-assisted decisions, which lens does this check belong to: “Did I ask the AI to test my preferred conclusion, or mainly to produce better arguments for it?”

  1. Illusory Truth Effect
  2. Automation Bias
  3. Confirmation Bias
Show the best first lens

Confirmation Bias

Did I ask the AI to test my preferred conclusion, or mainly to produce better arguments for it?

Evidence note Confirmation bias is an umbrella label for several ways existing beliefs or hypotheses can influence information search and interpretation. It should not be reduced to one behaviour such as reading only agreeable news, and a preference for confirming tests is not irrational in every task or environment.

Read the evidence review · Open the decision guide

Exercise 6 of 6

In ai-assisted decisions, which lens does this check belong to: “Does this claim feel more credible because I have encountered it repeatedly, or because I verified independent evidence for it?”

  1. Anchoring Effect
  2. Illusory Truth Effect
  3. Automation Bias
Show the best first lens

Illusory Truth Effect

Does this claim feel more credible because I have encountered it repeatedly, or because I verified independent evidence for it?

Evidence note Repeated information is, on average, judged as more truthful than comparable new information. The size of the effect varies with the material and procedure, and repetition does not make every claim believable or erase all prior knowledge.

Read the evidence review · Open the decision guide